Towards accessible auditory health: A cloud-based fNIRS solution for auditory training and assessment

Huang, Qiqi, Liu, Jiang, Li, Yang, Zhao, Linqi, Stawarz, Katarzyna ORCID: https://orcid.org/0000-0001-9021-0615 and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2025. Towards accessible auditory health: A cloud-based fNIRS solution for auditory training and assessment. IEEE Transactions on Instrumentation and Measurement 74 , 4511612. 10.1109/TIM.2025.3580795

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB)

Abstract

Auditory training (AT) is a proactive intervention for managing auditory health and preventing hearing loss. However, in its current form, it requires significant financial and time resources. As the excellent performance of functional near-infrared spectroscopy (fNIRS) in the medical field has led to its gradual application in auditory health, we aim to combine machine learning with fNIRS data to enhance accessibility and general applicability of AT. In this study, fNIRS was used to collect brain data related to auditory tasks and six machine learning methods were applied to classify different AT outcomes. Among these algorithms, AdaBoost demonstrated the best performance, achieving an accuracy of 88%. Based on the results, we propose a novel cloud-based framework that integrates AT with the assessment of training outcomes for individuals with hearing loss. The framework has been validated for its generalizability, and the evaluation results are not influenced by subjective experience.

Item Type:	Article
Date Type:	Publication
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9456
Funders:	China Scholarship Council (CSC) under Grant No. 202306220080
Date of First Compliant Deposit:	30 June 2025
Date of Acceptance:	6 June 2025
Last Modified:	14 Jul 2025 09:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179421

Actions (repository staff only)

Edit Item